Introduction And Objectives: Tako-tsubo syndrome produces a variable degree of transient left ventricular dysfunction. Our objective was to determine the short- and long-term prognosis of this syndrome, the incidence of and risk factors for the development of heart failure, and the influence on heart failure on the long-term outcome in our patient population.
Methods: We prospectively recorded the clinical features and events during the hospital stay and follow-up of 100 patients with tako-tsubo syndrome. The risk factors for heart failure during hospital stay, considered as Killip class≥II, were assessed.
Results: Most of the patients were women (89%), with a mean age of 68 years. The distribution according to Killip class was: Killip I, 70 patients; Killip II, 15; Killip III, 5; and Killip IV, 10. Cardiovascular risk factors, including diabetes, were common in the overall group, but were more so in the heart failure cohort. The left ventricular ejection fraction was lower in the heart failure group (51% vs 42%; P<.01). There were no differences in preadmission medications or biomarkers of necrosis. Over a median follow-up of 1380 days, the incidence of events reported during the hospital stay and long-term follow-up, both for death and the combined endpoints, was higher in the heart failure cohort.
Conclusions: Although the prognosis in tako-tsubo syndrome is usually good, heart failure occurs quite frequently, mainly in patients with a greater number of comorbidities and poorer previous functional class. Moreover, heart failure is associated with a higher number of early and late adverse events. The overall long-term prognosis is good. Full English text available from:www.revespcardiol.org.
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http://dx.doi.org/10.1016/j.recesp.2012.04.016 | DOI Listing |
Acta Pharm
December 2024
Department of Clinical Pharmacy, University Hospital Dubrava, 10000 Zagreb Croatia.
Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity globally. It is estimated that 17.9 million people died from CVDs in 2019, which represents 32 % of all deaths worldwide.
View Article and Find Full Text PDFEur Heart J Cardiovasc Imaging
January 2025
Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy.
Aim: Computed tomography (CT)-derived extracellular volume fraction (ECV) is a non-invasive method to quantify myocardial fibrosis. Evaluating CT-ECV during aortic valve replacement (AVR) planning CT in severe aortic stenosis (AS) may aid prognostic stratification. This meta-analysis evaluated the prognostic significance of CT-ECV in severe AS necessitating AVR.
View Article and Find Full Text PDFASAIO J
December 2024
Cleveland Clinic Florida, Heart, Vascular and Thoracic Institute, Advanced Heart Failure Program, Weston, Florida.
We investigated the association of preimplant left ventricular end-diastolic diameter (LVEDD) with outcomes after HeartMate 3 (HM3) left ventricular assist device (LVAD) implantation. Patients from the European Registry for Patients with Mechanical Circulatory Support (EUROMACS) registry who underwent HM3 implantation from August 2014 to February 2023 (n = 834) were analyzed according to preoperative LVEDD: less than or equal to 65 (n = 251), 65-80 (n = 441), and greater than or equal to 80 mm (n = 142). The mean age was 54.
View Article and Find Full Text PDFJ Clin Endocrinol Metab
January 2025
Department of Neurosurgery and State Key Laboratory of Trauma, Burn and Combined Injury, Southwest Hospital; Chongqing Key Laboratory of Precision Neuromedicine and Neuroregenaration, Third Military Medical University (Army Medical University), 400038 Chongqing, China.
Background: Phthalates, widely used as chemical additives, are often found as mixtures in the environment. However, the combined impact of phthalate exposure on sarcopenia remains unclear.
Objective: This study aimed to investigate the relationships between phthalates and sarcopenia in adults.
Eur Heart J Acute Cardiovasc Care
January 2025
Department of Medical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.
Background: Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and limitations of traditional diagnostic methods. This study aimed to develop and evaluate a deep-learning model using electrocardiogram (ECG) data to enhance AHF identification in the ER.
Methods: In this retrospective cohort study, we analyzed the ECG data of 19,285 patients who visited ERs of three hospitals between 2016 and 2020; 9,119 with available left ventricular ejection fraction and N-terminal prohormone of brain natriuretic peptide level data and who were diagnosed with AHF were included in the study.
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